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Residential building archetype classification to drive Net-Zero energy planning for the Canadian residential building stock

Residential building archetype classification to drive Net-Zero energy planning for the Canadian residential building stock
住宅建筑原型分类,以推动加拿大住宅建筑存量的净零能源规划
批准号:
RGPIN-2019-06124
负责人:
Richman, Russell
金额:
$2.26万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31

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中文摘要
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英文摘要
Society's growing energy demand and emerging policy crisis regarding greenhouse gas (GHG) emissions requires authorities and decision makers to comprehensively long-term plan for urban and sub-urban areas. A current Intergovernmental Panel on Climate Change (IPCC) report has called for ".rapid, far-reaching and unprecedented changes in all aspects of society" with a requirement for net-zero buildings by 2050. The proposed research will develop a bottom-up framework to create residential building archetypes in order to support a Canadian residential retrofit strategy to reach net-zero in the next 20 to 30 years. ***Nature of the Work*** The primary objective of this research program is to reduce the energy and GHG impact of the Canadian residential housing stock through providing quantified data to support the generation of a national policy resulting in a Canadian retrofit plan for net-zero residential buildings. The research will use field studies and laboratory testing to develop bottom-up residential housing archetypes capable of supporting a national net-zero retrofit plan. Archetypes will be developed at a community, city and provincial level as part of the proposed research. At each level, a net-zero retrofit plan will be developed in order to show the proposed research's scalability and ability to be used at a national level in the long term. ***Why is the Research Important*** Canada relies too much on cheap, readily available energy and emits overbearing amounts of GHG's. This research will develop a path to retrofit residential buildings to a level where their impact is net-zero across any given year. Research of this nature is not completed in Canada and has limited activity globally. ***To Whom is the Research Important*** The research is important to the general public, industry and academia. The general public will benefit from development of the net-zero retrofit plan and its associated archetypes. Industry will benefit from direct market growth in green economy sectors such as sustainable materials, construction and renewables. Academia will benefit from leading edge research providing concrete results, archetype development and optimizing retrofit strategies. ***Anticipated Outcomes*** - A framework to develop bottom-up archetypes at a national level to support optimal net-zero retrofit plans. - An exemplary case study showing how the proposed research can make an existing community net-zero ready. ***How the Research Field and Canada Will Benefit*** The proposed project will reduce Canada's environmental impacts from green house gas emission. The resulting target energy reductions will directly enable Canada to achieve their GHG emission reductions in the next 20-30 years.
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Residential building archetype classification to drive Net-Zero energy planning for the Canadian residential building stock
  • 批准号:
    RGPIN-2019-06124
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2022
  • 负责人:
    Richman, Russell
  • 依托单位:
Prediction of the Coupled Hygrothermal and Movement Behaviour of Mass Timber Buildings - A Laboratory to Construction to Post-Occupancy Case-Study Based Numerical Model
  • 批准号:
    558275-2020
  • 项目类别:
    Alliance Grants
  • 资助金额:
    $8.01万
  • 财政年份:
    2021
  • 负责人:
    Richman, Russell
  • 依托单位:
Residential building archetype classification to drive Net-Zero energy planning for the Canadian residential building stock
  • 批准号:
    RGPIN-2019-06124
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2020
  • 负责人:
    Richman, Russell
  • 依托单位:
Optimizing Building Operations using Machine Learning to Minimize Stack Effect
  • 批准号:
    543443-2019
  • 项目类别:
    Engage Grants Program
  • 资助金额:
    $1.82万
  • 财政年份:
    2019
  • 负责人:
    Richman, Russell
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  • 项目类别:
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